The future of AI — whether in training or evaluation, classical ML or agentic workflows — starts with high-quality data.

At HumanSignal, we’re building the platform that powers the creation, curation, and evaluation of that data. From fine-tuning foundation models to validating agent behaviors in production, our tools are used by leading AI teams to ensure models are grounded in real-world signal, not noise.

Our open-source product, Label Studio, has become the de facto standard for labeling and evaluating data across modalities — from text and images to time series and agents-in-environments. With over 250,000 users and hundreds of millions of labeled samples, it’s the most widely adopted OSS solution for teams working on building AI systems. 

Label Studio Enterprise builds on that traction with the security, collaboration, and scalability features needed to support mission-critical AI pipelines — powering everything from model training datasets to eval test sets to continuous feedback loops.We started before foundation models were mainstream, and we’re doubling down now that AI is eating the world. If you're excited to help leading AI teams build smarter, more accurate systems — we’d love to talk.

Graphic Design Expert

Location: Remote
Type: Part-Time Contract (15+ hours per week for 2 weeks | 30 hour minimum commitment)
Duration: 2 weeks with potential for ongoing work
Compensation: Commensurate with experience

About the Project

You'll evaluate and rate graphic design elements on standardized quality scales to train AI models that assess design effectiveness—contributing to cutting-edge technology that advances how AI understands and evaluates visual content quality.

What You'll Do

• Review and rate graphic design elements using our Label Studio Enterprise platform across multiple quality dimensions:
  • Typography quality (readability, hierarchy, font pairing appropriateness)
  • Visual hierarchy effectiveness (clarity, balance, focal point strength)
  • Design element execution (icon quality, illustration polish, image selection)
  • Color palette effectiveness (harmony, contrast, accessibility)
  • Layout and composition quality (grid usage, spacing, alignment)
  • Overall design effectiveness and professional polish
• Apply consistent 1-5 rating scales across hundreds of design examples
• Provide clear, objective assessments based on established design principles
• Follow detailed rating guidelines and rubrics to maintain consistency
• Process high volumes of design samples with sustained attention to detail
• Participate in weekly calibration sessions to align rating standards
• Meet quality benchmarks for rating consistency and inter-rater reliability

Basic Qualifications

Required Experience

• Formal training in graphic design, visual arts, or related field (degree, certificate, or bootcamp completion) OR 2-3 years of professional graphic design experience
• Strong proficiency in design principles (composition, color theory, typography, visual hierarchy)
• Ability to objectively critique and evaluate design work against industry standards
• Understanding of what constitutes effective vs. ineffective design choices
• Experience reviewing or providing design feedback
• Exceptional attention to detail and ability to maintain focus during repetitive tasks

Skills & Attributes

• Comfort with highly repetitive work—ability to maintain quality and engagement while rating similar content for extended periods
• Strong visual discrimination and consistent aesthetic judgment
• Reliability and consistency in applying rating standards
• Self-motivated with ability to work independently on repetitive tasks
• Excellent focus and concentration for sustained periods
• Strong communication skills for flagging edge cases or unclear examples
• Comfortable with feedback and recalibration
• Minimum 10 hours per week availability for at least 3 months

Nice-to-Haves

Preferred Background

• Portfolio demonstrating diverse design work and critical eye
• Experience as a design reviewer, art director, or in quality assurance roles
• Understanding of accessibility standards (WCAG, inclusive design principles)
• Knowledge of design across multiple contexts (branding, editorial, digital, UX/UI)
• Experience teaching or mentoring design concepts

Technical Experience

• Experience with Figma, Sketch, or other modern design tools
• Prior data annotation, content moderation, or quality evaluation experience
• Exposure to machine learning or computer vision concepts
• Familiarity with design systems and component libraries

Work Style

• High tolerance for repetitive evaluation tasks—finds rhythm and satisfaction in systematic work
• Ability to maintain consistent standards across thousands of ratings
• Strong pattern recognition—quickly identifying quality patterns across examples
• Disciplined, methodical approach with minimal variation in judgment
• Problem-solving mindset for handling ambiguous or borderline cases
• Interest in AI/ML applications in design and creative fields
• Previous remote work experience with self-directed tasks

What Makes Someone Successful in This Role

You thrive on consistency and repetition - You find satisfaction in systematic work and can maintain quality standards while evaluating similar content repeatedly without losing focus or accuracy.

You have a calibrated design eye - You can quickly assess design quality against objective criteria and apply the same standards consistently across hundreds or thousands of examples.

You're objective and principled - You separate personal taste from professional assessment, basing ratings on established design principles rather than subjective preference.

You maintain sustained focus - You can work through high volumes of repetitive tasks while staying sharp and attentive to subtle quality differences.

You're a clear communicator - You flag confusing examples or rating criteria early, ask clarifying questions, and document edge cases clearly.

You're curious about technology - You're genuinely interested in how AI learns to evaluate design quality and see value in contributing to machine learning training data.